""" ResNeSt Models

Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955

Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang

Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
import torch
from torch import nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import SplitAttn
from .registry import register_model
from .resnet import ResNet


def _cfg(url="", **kwargs):
    return {
        "url": url,
        "num_classes": 1000,
        "input_size": (3, 224, 224),
        "pool_size": (7, 7),
        "crop_pct": 0.875,
        "interpolation": "bilinear",
        "mean": IMAGENET_DEFAULT_MEAN,
        "std": IMAGENET_DEFAULT_STD,
        "first_conv": "conv1.0",
        "classifier": "fc",
        **kwargs,
    }


default_cfgs = {
    "resnest14d": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth"
    ),
    "resnest26d": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth"
    ),
    "resnest50d": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50-528c19ca.pth"
    ),
    "resnest101e": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth",
        input_size=(3, 256, 256),
        pool_size=(8, 8),
    ),
    "resnest200e": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest200-75117900.pth",
        input_size=(3, 320, 320),
        pool_size=(10, 10),
        crop_pct=0.909,
        interpolation="bicubic",
    ),
    "resnest269e": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest269-0cc87c48.pth",
        input_size=(3, 416, 416),
        pool_size=(13, 13),
        crop_pct=0.928,
        interpolation="bicubic",
    ),
    "resnest50d_4s2x40d": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_4s2x40d-41d14ed0.pth",
        interpolation="bicubic",
    ),
    "resnest50d_1s4x24d": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_1s4x24d-d4a4f76f.pth",
        interpolation="bicubic",
    ),
}


class ResNestBottleneck(nn.Module):
    """ResNet Bottleneck"""

    # pylint: disable=unused-argument
    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        radix=1,
        cardinality=1,
        base_width=64,
        avd=False,
        avd_first=False,
        is_first=False,
        reduce_first=1,
        dilation=1,
        first_dilation=None,
        act_layer=nn.ReLU,
        norm_layer=nn.BatchNorm2d,
        attn_layer=None,
        aa_layer=None,
        drop_block=None,
        drop_path=None,
    ):
        super(ResNestBottleneck, self).__init__()
        assert reduce_first == 1  # not supported
        assert attn_layer is None  # not supported
        assert aa_layer is None  # TODO not yet supported
        assert drop_path is None  # TODO not yet supported

        group_width = int(planes * (base_width / 64.0)) * cardinality
        first_dilation = first_dilation or dilation
        if avd and (stride > 1 or is_first):
            avd_stride = stride
            stride = 1
        else:
            avd_stride = 0
        self.radix = radix
        self.drop_block = drop_block

        self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
        self.bn1 = norm_layer(group_width)
        self.act1 = act_layer(inplace=True)
        self.avd_first = (
            nn.AvgPool2d(3, avd_stride, padding=1)
            if avd_stride > 0 and avd_first
            else None
        )

        if self.radix >= 1:
            self.conv2 = SplitAttn(
                group_width,
                group_width,
                kernel_size=3,
                stride=stride,
                padding=first_dilation,
                dilation=first_dilation,
                groups=cardinality,
                radix=radix,
                norm_layer=norm_layer,
                drop_block=drop_block,
            )
            self.bn2 = nn.Identity()
            self.act2 = nn.Identity()
        else:
            self.conv2 = nn.Conv2d(
                group_width,
                group_width,
                kernel_size=3,
                stride=stride,
                padding=first_dilation,
                dilation=first_dilation,
                groups=cardinality,
                bias=False,
            )
            self.bn2 = norm_layer(group_width)
            self.act2 = act_layer(inplace=True)
        self.avd_last = (
            nn.AvgPool2d(3, avd_stride, padding=1)
            if avd_stride > 0 and not avd_first
            else None
        )

        self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False)
        self.bn3 = norm_layer(planes * 4)
        self.act3 = act_layer(inplace=True)
        self.downsample = downsample

    def zero_init_last_bn(self):
        nn.init.zeros_(self.bn3.weight)

    def forward(self, x):
        shortcut = x

        out = self.conv1(x)
        out = self.bn1(out)
        if self.drop_block is not None:
            out = self.drop_block(out)
        out = self.act1(out)

        if self.avd_first is not None:
            out = self.avd_first(out)

        out = self.conv2(out)
        out = self.bn2(out)
        if self.drop_block is not None:
            out = self.drop_block(out)
        out = self.act2(out)

        if self.avd_last is not None:
            out = self.avd_last(out)

        out = self.conv3(out)
        out = self.bn3(out)
        if self.drop_block is not None:
            out = self.drop_block(out)

        if self.downsample is not None:
            shortcut = self.downsample(x)

        out += shortcut
        out = self.act3(out)
        return out


def _create_resnest(variant, pretrained=False, **kwargs):
    return build_model_with_cfg(
        ResNet, variant, pretrained, default_cfg=default_cfgs[variant], **kwargs
    )


@register_model
def resnest14d(pretrained=False, **kwargs):
    """ResNeSt-14d model. Weights ported from GluonCV."""
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[1, 1, 1, 1],
        stem_type="deep",
        stem_width=32,
        avg_down=True,
        base_width=64,
        cardinality=1,
        block_args=dict(radix=2, avd=True, avd_first=False),
        **kwargs
    )
    return _create_resnest("resnest14d", pretrained=pretrained, **model_kwargs)


@register_model
def resnest26d(pretrained=False, **kwargs):
    """ResNeSt-26d model. Weights ported from GluonCV."""
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[2, 2, 2, 2],
        stem_type="deep",
        stem_width=32,
        avg_down=True,
        base_width=64,
        cardinality=1,
        block_args=dict(radix=2, avd=True, avd_first=False),
        **kwargs
    )
    return _create_resnest("resnest26d", pretrained=pretrained, **model_kwargs)


@register_model
def resnest50d(pretrained=False, **kwargs):
    """ResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955
    Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample.
    """
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[3, 4, 6, 3],
        stem_type="deep",
        stem_width=32,
        avg_down=True,
        base_width=64,
        cardinality=1,
        block_args=dict(radix=2, avd=True, avd_first=False),
        **kwargs
    )
    return _create_resnest("resnest50d", pretrained=pretrained, **model_kwargs)


@register_model
def resnest101e(pretrained=False, **kwargs):
    """ResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955
    Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
    """
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[3, 4, 23, 3],
        stem_type="deep",
        stem_width=64,
        avg_down=True,
        base_width=64,
        cardinality=1,
        block_args=dict(radix=2, avd=True, avd_first=False),
        **kwargs
    )
    return _create_resnest("resnest101e", pretrained=pretrained, **model_kwargs)


@register_model
def resnest200e(pretrained=False, **kwargs):
    """ResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955
    Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
    """
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[3, 24, 36, 3],
        stem_type="deep",
        stem_width=64,
        avg_down=True,
        base_width=64,
        cardinality=1,
        block_args=dict(radix=2, avd=True, avd_first=False),
        **kwargs
    )
    return _create_resnest("resnest200e", pretrained=pretrained, **model_kwargs)


@register_model
def resnest269e(pretrained=False, **kwargs):
    """ResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955
    Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
    """
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[3, 30, 48, 8],
        stem_type="deep",
        stem_width=64,
        avg_down=True,
        base_width=64,
        cardinality=1,
        block_args=dict(radix=2, avd=True, avd_first=False),
        **kwargs
    )
    return _create_resnest("resnest269e", pretrained=pretrained, **model_kwargs)


@register_model
def resnest50d_4s2x40d(pretrained=False, **kwargs):
    """ResNeSt-50 4s2x40d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md"""
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[3, 4, 6, 3],
        stem_type="deep",
        stem_width=32,
        avg_down=True,
        base_width=40,
        cardinality=2,
        block_args=dict(radix=4, avd=True, avd_first=True),
        **kwargs
    )
    return _create_resnest("resnest50d_4s2x40d", pretrained=pretrained, **model_kwargs)


@register_model
def resnest50d_1s4x24d(pretrained=False, **kwargs):
    """ResNeSt-50 1s4x24d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md"""
    model_kwargs = dict(
        block=ResNestBottleneck,
        layers=[3, 4, 6, 3],
        stem_type="deep",
        stem_width=32,
        avg_down=True,
        base_width=24,
        cardinality=4,
        block_args=dict(radix=1, avd=True, avd_first=True),
        **kwargs
    )
    return _create_resnest("resnest50d_1s4x24d", pretrained=pretrained, **model_kwargs)
